Those of you who've taken political science know that all of human history in political science is the division between the individual and the state. Right? The person and the group, right? And these are the two divides. And the whole purpose of politics is to find some sort of accommodation for them or if you're Ayn Rand, to favor the individual and ignore the group.<br><br> And it seems to me that networks offers that middle way. Networks offers that path that isn't the individual and isn't the group, doesn't force you to choose between the individual and the group.

But more or less, a group is a collection of entities or members according to their nature or their feature or their properties or whatever, their essential nature, maybe, their accidental nature, maybe, whatever, but according to their nature. What defines a group is the quality the members possess in common and then the number of members in that group. Groups are about nature, they're about quality, they're about mass. They're about number. <br><br>A network, by contrast, is an association – I use that word very precisely – an association of entities or members where this association is facilitated or created by a set of connections between those entities. And if you say, "Well what is a connection?" A connection is merely some conduit along which a signal can run. Well, that clarified it, didn't it? What defines a network is the nature and the extent of this connectivity. The nature and the extent to which these individuals are connected together.

I want to change the system of assessment in schools because right now we have tests and things like that that are scrupulously fair, particularly distance learning where we outline the objectives the performance metrics and the outcomes and all of that. I want to scrap that system. I want testing to be done by at random by comments from your peers and other people and strangers based on no criteria whatsoever and applied unequally and unfairly.

Networks are almost defined by the opposite, defined by their diversity. A network thrives on diversity. It wouldn't be a network without diversity.

Internet technology that encourages diversity rather than conformity includes things like personal home pages or these days, blogs. I should add to this slide MySpace profiles and things like that, your account on Flickr. All of these things that allows the individual to express themselves rather than the individual being part of some larger entity.

However, nobody would assume that in order to know that wood one has to know exactly every tree, one by one, its shape, age and location. Every plant. Every leave of every plant. Every animal and where every animal is and what every animal is doing at any instant. Every stone. Every particle.

We argue that learning occurs in networks, and therefore, that the properties of successful networks are also the properties of successful learning environments. We don't 'apply' this in any strict sense - we would never force people to be connectivists. Indeed, within the learning environment, we believe there should be diversity; we believe people should be free to choose their own form of learning.

To me, <span style="font-style: italic;">far more</span> complex - and insightful - forms of reasoning are being created through the interplay among thousands, or millions, of individual content elements. Where each content element may by itself appear to be <span style="font-style: italic;">simple</span>, it is the interconnections between them that creates a much more complex, deep, and <span style="font-style: italic;">rich</span> tapestry of meaning, <span style="font-style: italic;">far more</span> than could be created merely using linguistic devices.

It is substantially <span style="font-style: italic;">harder</span> to work with the disorder and complexity we see within a connectivist network. Because linguistic (syntactical and semantical) descriptions of the concepts and entities in such a network just barely touch the surface, and students must therefore immerse themselves in the process of reasoning in such a system, rather than merely reading about it.

<p>Downes’ and Siemens’ discussions shed new light on fundamental concepts, such as rules versus patterns, complicated vs. complex, equivalence vs. similarity, and coping with ambiguity and uncertainty. And these consideration render many entrenched practices of the entire knowledge industry questionable.</p> <p>All these aspects have one thing in common: that they can be illustrated by the neuronal <strong>metaphor</strong>, the metaphor of a network with nodes and connections, where</p> <blockquote><p><em>“Not all connections are of equal strength in this metaphor”</em> (<a href="http://en.wikipedia.org/wiki/Connectivism_%28learning_theory%29">Wikipedia</a>)</p></blockquote>

So, connectivism and its neuronal connections metaphor, allow to distinguish more clearly between two types of knowledge, one of which is the more adequate one for coping with complexity and uncertainty: <em>connective</em> knowledge.